Relaxing Jacobian matching in Gradient Checker test.
Any result of an arithmetic operation on floating-point matrices
should never be checked for strict equality with some expected
value, due to limited floating point precision on different machines.
This fixes some occurences of exact checks in the gradient checker
unit test that were causing problems on some platforms.
Change-Id: I48e804c9c705dc485ce74ddfe51037d4957c8fcb
diff --git a/internal/ceres/gradient_checker_test.cc b/internal/ceres/gradient_checker_test.cc
index 1c01df0..3d40a4d 100644
--- a/internal/ceres/gradient_checker_test.cc
+++ b/internal/ceres/gradient_checker_test.cc
@@ -37,9 +37,10 @@
#include <vector>
#include "ceres/cost_function.h"
+#include "ceres/problem.h"
#include "ceres/random.h"
#include "ceres/solver.h"
-#include "ceres/problem.h"
+#include "ceres/test_util.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
@@ -366,6 +367,13 @@
Matrix global_J_local;
};
+// Helper function to compare two Eigen matrices (used in the test below).
+void ExpectMatricesClose(Matrix p, Matrix q, double tolerance) {
+ ASSERT_EQ(p.rows(), q.rows());
+ ASSERT_EQ(p.cols(), q.cols());
+ ExpectArraysClose(p.size(), p.data(), q.data(), tolerance);
+}
+
TEST(GradientChecker, TestCorrectnessWithLocalParameterizations) {
// Create cost function.
Eigen::Vector3d residual_offset(100.0, 200.0, 300.0);
@@ -395,7 +403,7 @@
// Test cost function for correctness.
Eigen::Matrix<double, 3, 3, Eigen::RowMajor> j1_out;
Eigen::Matrix<double, 3, 2, Eigen::RowMajor> j2_out;
- Eigen::VectorXd residual(3);
+ Eigen::Vector3d residual;
std::vector<const double*> parameters(2);
parameters[0] = param0.data();
parameters[1] = param1.data();
@@ -408,7 +416,7 @@
EXPECT_TRUE(j1_out == j0);
EXPECT_TRUE(j2_out == j1);
- EXPECT_TRUE(residual.isApprox(residual_expected, kTolerance));
+ ExpectMatricesClose(residual, residual_expected, kTolerance);
// Create local parameterization.
Eigen::Matrix<double, 3, 2, Eigen::RowMajor> global_J_local;
@@ -430,7 +438,7 @@
Eigen::Vector3d x_plus_delta;
parameterization.Plus(x.data(), delta.data(), x_plus_delta.data());
Eigen::Vector3d x_plus_delta_expected = x + (global_J_local * delta);
- EXPECT_TRUE(x_plus_delta.isApprox(x_plus_delta_expected, kTolerance));
+ ExpectMatricesClose(x_plus_delta, x_plus_delta_expected, kTolerance);
// Now test GradientChecker.
std::vector<const LocalParameterization*> parameterizations(2);
@@ -466,18 +474,16 @@
ASSERT_EQ(results.return_value, true);
ASSERT_TRUE(results.residuals == residual);
CheckDimensions(results, parameter_sizes, local_parameter_sizes, 3);
- EXPECT_TRUE(results.local_jacobians.at(0) == j0 * global_J_local);
+ ExpectMatricesClose(results.local_jacobians.at(0), j0 * global_J_local,
+ kTolerance);
EXPECT_TRUE(results.local_jacobians.at(1) == j1);
- EXPECT_TRUE(results.local_numeric_jacobians.at(0).isApprox(
- j0 * global_J_local, kTolerance));
- EXPECT_TRUE(results.local_numeric_jacobians.at(1).isApprox(
- j1, kTolerance));
+ ExpectMatricesClose(results.local_numeric_jacobians.at(0),
+ j0 * global_J_local, kTolerance);
+ ExpectMatricesClose(results.local_numeric_jacobians.at(1), j1, kTolerance);
EXPECT_TRUE(results.jacobians.at(0) == j0);
EXPECT_TRUE(results.jacobians.at(1) == j1);
- EXPECT_TRUE(results.numeric_jacobians.at(0).isApprox(
- j0, kTolerance));
- EXPECT_TRUE(results.numeric_jacobians.at(1).isApprox(
- j1, kTolerance));
+ ExpectMatricesClose(results.numeric_jacobians.at(0), j0, kTolerance);
+ ExpectMatricesClose(results.numeric_jacobians.at(1), j1, kTolerance);
EXPECT_GE(results.maximum_relative_error, 0.0);
EXPECT_TRUE(results.error_log.empty());
@@ -504,16 +510,16 @@
CheckDimensions(results, parameter_sizes, local_parameter_sizes, 3);
ASSERT_EQ(results.local_jacobians.size(), 2);
ASSERT_EQ(results.local_numeric_jacobians.size(), 2);
- EXPECT_TRUE(results.local_jacobians.at(0) == (j0 + j0_offset) * global_J_local);
+ ExpectMatricesClose(results.local_jacobians.at(0),
+ (j0 + j0_offset) * global_J_local, kTolerance);
EXPECT_TRUE(results.local_jacobians.at(1) == j1);
- EXPECT_TRUE(
- results.local_numeric_jacobians.at(0).isApprox(j0 * global_J_local,
- kTolerance));
- EXPECT_TRUE(results.local_numeric_jacobians.at(1).isApprox(j1, kTolerance));
- EXPECT_TRUE(results.jacobians.at(0) == j0 + j0_offset);
+ ExpectMatricesClose(results.local_numeric_jacobians.at(0),
+ j0 * global_J_local, kTolerance);
+ ExpectMatricesClose(results.local_numeric_jacobians.at(1), j1, kTolerance);
+ ExpectMatricesClose(results.jacobians.at(0), j0 + j0_offset, kTolerance);
EXPECT_TRUE(results.jacobians.at(1) == j1);
- EXPECT_TRUE(results.numeric_jacobians.at(0).isApprox(j0, kTolerance));
- EXPECT_TRUE(results.numeric_jacobians.at(1).isApprox(j1, kTolerance));
+ ExpectMatricesClose(results.numeric_jacobians.at(0), j0, kTolerance);
+ ExpectMatricesClose(results.numeric_jacobians.at(1), j1, kTolerance);
EXPECT_GT(results.maximum_relative_error, 0.0);
EXPECT_FALSE(results.error_log.empty());
@@ -538,16 +544,17 @@
CheckDimensions(results, parameter_sizes, local_parameter_sizes, 3);
ASSERT_EQ(results.local_jacobians.size(), 2);
ASSERT_EQ(results.local_numeric_jacobians.size(), 2);
- EXPECT_TRUE(results.local_jacobians.at(0) ==
- (j0 + j0_offset) * parameterization.global_J_local);
+ ExpectMatricesClose(results.local_jacobians.at(0),
+ (j0 + j0_offset) * parameterization.global_J_local,
+ kTolerance);
EXPECT_TRUE(results.local_jacobians.at(1) == j1);
- EXPECT_TRUE(results.local_numeric_jacobians.at(0).isApprox(
- j0 * parameterization.global_J_local, kTolerance));
- EXPECT_TRUE(results.local_numeric_jacobians.at(1).isApprox(j1, kTolerance));
- EXPECT_TRUE(results.jacobians.at(0) == j0 + j0_offset);
+ ExpectMatricesClose(results.local_numeric_jacobians.at(0),
+ j0 * parameterization.global_J_local, kTolerance);
+ ExpectMatricesClose(results.local_numeric_jacobians.at(1), j1, kTolerance);
+ ExpectMatricesClose(results.jacobians.at(0), j0 + j0_offset, kTolerance);
EXPECT_TRUE(results.jacobians.at(1) == j1);
- EXPECT_TRUE(results.numeric_jacobians.at(0).isApprox(j0, kTolerance));
- EXPECT_TRUE(results.numeric_jacobians.at(1).isApprox(j1, kTolerance));
+ ExpectMatricesClose(results.numeric_jacobians.at(0), j0, kTolerance);
+ ExpectMatricesClose(results.numeric_jacobians.at(1), j1, kTolerance);
EXPECT_GE(results.maximum_relative_error, 0.0);
EXPECT_TRUE(results.error_log.empty());
